Table of contents for Handbook of computational econometrics / edited by David A. Belsley, Erricos Kontoghiorghes.
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List of Contributors
Preface
1 Econometric software
Charles G. Renfro
1.1 Introduction
1.2 The nature of econometric software
1.3 The existing characteristics of econometric software
1.4 Conclusion
Acknowledgments
References
2 The accuracy of econometric software
Bruce D. McCullough
2.1 Introduction
2.2 Inaccurate econometric results
2.3 Entry-level tests
2.4 Intermediate-level tests
2.5 Conclusions
Acknowledgments
References
3 Heuristic optimization methods in econometrics
Manfred Gilli and Peter Winker
3.1 Traditional numerical versus heuristic optimization methods
3.2 Heuristic optimization
3.3 Stochastics of the solution
3.4 General guidelines for the use of optimization heuristics
3.5 Selected applications
3.6 Conclusions
Acknowledgments
References
4 Algorithms for minimax and expected value optimization
Panos Parpas and BerçRustem
4.1 Introduction
4.2 An interior point algorithm
4.3 Global optimization of polynomial minimax problems
4.4 Expected value optimization
4.5 Evaluation framework for minimax robust policies and expected
value optimization
Acknowledgments
References
5 Nonparametric estimation
Rand R. Wilcox
5.1 Introduction
5.2 Density estimation
5.3 Nonparametric regression
5.4 Nonparametric inferential techniques
References
6 Bootstrap hypothesis testing
James G. MacKinnon
6.1 Introduction
6.2 Bootstrap and Monte Carlo tests
6.3 Finite-sample properties of bootstrap tests
6.4 Double bootstrap and fast double bootstrap tests
6.5 Bootstrap data generating processes
6.6 Multiple test statistics
6.7 Finite-sample properties of bootstrap supF tests
6.8 Conclusion
Acknowledgments
References
7 Simulation-based Bayesian econometric inference: principles and some recent computational advances
Lennart F. Hoogerheide, Herman K. van Dijk and Rutger D. van Oest
7.1 Introduction
7.2 A primer on Bayesian inference
7.3 Simulation methods
7.4 Concluding remarks
Acknowledgments
References
8 Econometric analysis with vector autoregressive models
Helmut Lütkepohl
8.1 Introduction
8.2 VAR processes
8.3 Estimation of VAR models
8.4 Model specification
8.5 Model checking
8.6 Forecasting
8.7 Causality analysis
8.8 Structural VARs and impulse response analysis
8.9 Conclusions and extensions
Acknowledgments
References
9 Statistical signal extraction and filtering: a partial survey
D. Stephen G. Pollock
9.1 Introduction: the semantics of filtering
9.2 Linear and circular convolutions
9.3 Local polynomial regression
9.4 The concepts of the frequency domain
9.5 The classical Wiener–Kolmogorov theory
9.6 Matrix formulations
9.7 Wiener–Kolmogorov filtering of short stationary sequences
9.8 Filtering nonstationary sequences
9.9 Filtering in the frequency domain
9.10 Structural time-series models
9.11 The Kalman filter and the smoothing algorithm
References
10 Concepts of and tools for nonlinear time-series modelling
Alessandra Amendola and Christian Francq
10.1 Introduction
10.2 Nonlinear data generating processes and linear models
10.3 Testing linearity
10.4 Probabilistic tools
10.5 Identification, estimation and model adequacy checking
10.6 Forecasting with nonlinear models
10.7 Algorithmic aspects
10.8 Conclusion
Acknowledgments
References
11 Network economics
Anna Nagurney
11.1 Introduction
11.2 Variational inequalities
11.3 Transportation networks: user optimization versus system optimization
11.4 Spatial price equilibria
11.5 General economic equilibrium
11.6 Oligopolistic market equilibria
11.7 Variational inequalities and projected dynamical systems
11.8 Dynamic transportation networks
11.9 Supernetworks: applications to telecommuting decision making and teleshopping decision making
11.10 Supply chain networks and other applications
Acknowledgments
References
Index
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